{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('RentListingInquries_FE_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_label = train_data['interest_level']\n",
    "train_data.drop(['interest_level'],axis = 1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 首先对在特征处理过程中生成的count vector的数据列进行主成分分析. 目的数为了提高分析性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49352, 11)\n",
      "[[ -6.92278585e-04  -4.63523011e-04   1.59404533e-04 ...,   9.56124553e-04\n",
      "   -3.02162803e-04   1.70587608e-04]\n",
      " [ -3.46003601e-04  -1.59716763e-04   2.21949759e-05 ...,  -1.02237188e-03\n",
      "   -6.23512784e-05   6.26770305e-04]\n",
      " [  8.71653697e-04   4.88374976e-04   6.64186308e-04 ...,   7.09455245e-03\n",
      "    2.12029407e-04   1.60125282e-04]\n",
      " ..., \n",
      " [  1.46062443e-03   5.96209729e-04   3.47881592e-04 ...,   4.92267645e-03\n",
      "    5.58074514e-04  -1.60978648e-03]\n",
      " [  4.36205295e-04   5.15351492e-04   6.19834912e-04 ...,   1.72485056e-03\n",
      "    4.11467151e-04   1.24579202e-03]\n",
      " [  8.48356607e-04  -3.66707563e-04   5.45602368e-04 ...,   5.64360484e-04\n",
      "   -4.16580984e-04   7.75738140e-05]]\n",
      "Dimension 1 mainly element: allowed       0.26734\n",
      "building      0.29580\n",
      "center        0.24134\n",
      "dishwasher    0.23140\n",
      "doorman       0.25518\n",
      "elevator      0.24142\n",
      "fitness       0.24232\n",
      "laundry       0.44854\n",
      "Name: Dimension 1, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:24: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    },
    {
     "data": {
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qIuJyuWTjxo0RIiKbNm2KMplMrSIiYWFhPTabTeXJngEA8AQCHADA47Kzs21O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\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UAIDLRAIHAKjjQtP+e8Ly5ctrlR9//PE65xQWFtYqn+09O+uZZ57RM888U+e66667TuvW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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x135d785c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=0.8)\n",
    "feature_data = train_data.iloc[:,26:]\n",
    "feature_data_col = feature_data.columns\n",
    "# print(feature_data)\n",
    "pca.fit(feature_data)\n",
    "\n",
    "pca_train_data = pca.transform(feature_data)\n",
    "\n",
    "# 降维后的特征维数\n",
    "print(pca_train_data.shape)\n",
    "print(pca.components_)\n",
    "\n",
    "def pca_results(event_data, pca):\n",
    "    dimensions =  ['Dimension {}'.format(i) for i in range(1,len(pca.components_)+1)]\n",
    "\n",
    "    # PCA components个数\n",
    "    components = pd.DataFrame(np.round(pca.components_, 5), columns = event_data.keys())\n",
    "    components.index = dimensions\n",
    "\n",
    "    # 创建bar plot图\n",
    "    fig, ax = plt.subplots(figsize = (14,10))\n",
    "    \n",
    "    print('Dimension 1 mainly element:',components.loc['Dimension 1'].ix[components.iloc[0,:] > 0.2])\n",
    "\n",
    "    components.plot(ax = ax, kind = 'bar');\n",
    "    ax.set_ylabel(\"Feature Weights\")\n",
    "    ax.set_xticklabels(dimensions, rotation=0)\n",
    "\n",
    "    # 显示explained variance ratios\n",
    "    for i, ev in enumerate(pca.explained_variance_ratio_):\n",
    "        ax.text(i-0.3, ax.get_ylim()[1], \"Explained Variance\\n%.4f\"%(ev))\n",
    "\n",
    "pca_result_pic = pca_results(feature_data, pca)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可以看到第一个主成分对应的主要的词汇是: 'allowed',''building\",\"center\",\"dishwasher\",\"doorman\",\"elevator\",\"fitness\",“laundry\"这  些词汇看起来都和房子周边的生活、设施等有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 合并到训练数据中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.drop(feature_data_col,axis = 1, inplace = True)\n",
    "pca_train_df = pd.DataFrame(pca_train_data,columns = ['f1','f2','f3','f4','f5','f6','f7','f8','f9','f10','f11'],index=train_data.index)\n",
    "train_data = pd.concat([train_data,pca_train_df],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   bathrooms  bedrooms  manager_skill  top_10_manager  top_25_manager  \\\n",
      "0        1.5         3       0.255556               1               1   \n",
      "1        1.0         1       0.255556               1               1   \n",
      "2        1.0         1       0.255556               1               1   \n",
      "3        1.0         1       0.255556               1               1   \n",
      "4        1.0         1       0.255556               1               1   \n",
      "5        1.0         1       0.255556               1               1   \n",
      "6        1.0         2       0.255556               1               1   \n",
      "7        1.0         2       0.255556               1               1   \n",
      "8        1.0         1       0.255556               1               1   \n",
      "9        1.0         1       0.381366               1               1   \n",
      "\n",
      "   top_5_manager  top_50_manager  top_1_manager  top_2_manager  \\\n",
      "0              1               1              0              0   \n",
      "1              1               1              0              0   \n",
      "2              1               1              0              0   \n",
      "3              1               1              0              0   \n",
      "4              1               1              0              0   \n",
      "5              1               1              0              0   \n",
      "6              1               1              0              0   \n",
      "7              1               1              0              0   \n",
      "8              1               1              0              0   \n",
      "9              1               1              1              1   \n",
      "\n",
      "   top_15_manager    ...           f2        f3        f4        f5        f6  \\\n",
      "0               1    ...    -0.547180  0.415885  0.133458 -0.001555  0.029681   \n",
      "1               1    ...    -0.547180  0.415885  0.133458 -0.001555  0.029681   \n",
      "2               1    ...    -0.547180  0.415885  0.133458 -0.001555  0.029681   \n",
      "3               1    ...    -0.577644  0.484785  0.019683 -0.070522  0.063875   \n",
      "4               1    ...    -0.893431  0.613012  0.327786  0.501661 -0.844229   \n",
      "5               1    ...    -0.893431  0.613012  0.327786  0.501661 -0.844229   \n",
      "6               1    ...    -0.893431  0.613012  0.327786  0.501661 -0.844229   \n",
      "7               1    ...    -0.893431  0.613012  0.327786  0.501661 -0.844229   \n",
      "8               1    ...    -0.893431  0.613012  0.327786  0.501661 -0.844229   \n",
      "9               1    ...     1.638338 -0.096762  0.190253 -0.118465  0.047244   \n",
      "\n",
      "         f7        f8        f9       f10       f11  \n",
      "0  0.345547  0.003199 -0.173660 -0.002033  0.088326  \n",
      "1  0.345547  0.003199 -0.173660 -0.002033  0.088326  \n",
      "2  0.345547  0.003199 -0.173660 -0.002033  0.088326  \n",
      "3  0.677464  0.059904 -0.255292 -0.064309  0.050943  \n",
      "4  0.049589  0.627561  0.061510  0.082593  0.288369  \n",
      "5  0.049589  0.627561  0.061510  0.082593  0.288369  \n",
      "6  0.049589  0.627561  0.061510  0.082593  0.288369  \n",
      "7  0.049589  0.627561  0.061510  0.082593  0.288369  \n",
      "8  0.049589  0.627561  0.061510  0.082593  0.288369  \n",
      "9  0.485704 -0.396233  0.608250  0.300575  0.095804  \n",
      "\n",
      "[10 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "print(train_data.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 Logistics Regression的训练和调参\n",
    "测试下来，lbfgs的性能要比liblinear的性能要好一下。这是个多分类的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "penaltys = ['l2']\n",
    "c_logistic = [0.0001,0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "solver = ['lbfgs'] # lbfgs liblinear\n",
    "multi_class =['multinomial'] #multinomial ovr\n",
    "tuned_parameters = dict(penalty = penaltys, C = c_logistic,solver=solver,multi_class=multi_class,)\n",
    "grid_search_lg = GridSearchCV(LogisticRegression(random_state=1),param_grid=tuned_parameters,cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 模型训练和得分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time.struct_time(tm_year=2018, tm_mon=6, tm_mday=9, tm_hour=7, tm_min=21, tm_sec=19, tm_wday=5, tm_yday=160, tm_isdst=0)\n",
      "0.831232776787\n",
      "{'C': 10, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "{'mean_fit_time': array([ 1.50460711,  1.85128736,  1.84741588,  1.87965121,  1.81522746,\n",
      "        1.99159985,  2.16625104,  2.2752212 ]), 'std_fit_time': array([ 0.10932719,  0.19148443,  0.12413191,  0.17473047,  0.14351268,\n",
      "        0.15083478,  0.28226329,  0.16517836]), 'mean_score_time': array([ 0.00440693,  0.00481224,  0.00521393,  0.00561566,  0.00571556,\n",
      "        0.00701685,  0.01013079,  0.00671902]), 'std_score_time': array([ 0.00049531,  0.00121117,  0.00092974,  0.0013961 ,  0.001725  ,\n",
      "        0.00243522,  0.00424796,  0.0014742 ]), 'param_C': masked_array(data = [0.0001 0.001 0.01 0.1 1 10 100 1000],\n",
      "             mask = [False False False False False False False False],\n",
      "       fill_value = ?)\n",
      ", 'param_multi_class': masked_array(data = ['multinomial' 'multinomial' 'multinomial' 'multinomial' 'multinomial'\n",
      " 'multinomial' 'multinomial' 'multinomial'],\n",
      "             mask = [False False False False False False False False],\n",
      "       fill_value = ?)\n",
      ", 'param_penalty': masked_array(data = ['l2' 'l2' 'l2' 'l2' 'l2' 'l2' 'l2' 'l2'],\n",
      "             mask = [False False False False False False False False],\n",
      "       fill_value = ?)\n",
      ", 'param_solver': masked_array(data = ['lbfgs' 'lbfgs' 'lbfgs' 'lbfgs' 'lbfgs' 'lbfgs' 'lbfgs' 'lbfgs'],\n",
      "             mask = [False False False False False False False False],\n",
      "       fill_value = ?)\n",
      ", 'params': [{'C': 0.0001, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}, {'C': 0.001, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}, {'C': 0.01, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}, {'C': 0.1, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}, {'C': 1, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}, {'C': 10, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}, {'C': 100, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}, {'C': 1000, 'multi_class': 'multinomial', 'penalty': 'l2', 'solver': 'lbfgs'}], 'split0_test_score': array([ 0.69607942,  0.72839631,  0.74713808,  0.75058251,  0.75119036,\n",
      "        0.75270996,  0.75027859,  0.75068382]), 'split1_test_score': array([ 0.69678857,  0.74916422,  0.76891906,  0.77084389,  0.77094519,\n",
      "        0.77216088,  0.77337656,  0.77165434]), 'split2_test_score': array([ 0.70651403,  0.79404316,  0.82554959,  0.83213454,  0.83102016,\n",
      "        0.83264107,  0.83142539,  0.83193192]), 'split3_test_score': array([ 0.71522642,  0.85989261,  0.86668017,  0.86394489,  0.86151352,\n",
      "        0.86465404,  0.86303313,  0.86323574]), 'split4_test_score': array([ 0.71747061,  0.93382651,  0.93686664,  0.93342116,  0.9343332 ,\n",
      "        0.93402919,  0.9343332 ,  0.93463721]), 'mean_test_score': array([ 0.70641514,  0.81305722,  0.82902415,  0.83017912,  0.82979413,\n",
      "        0.83123278,  0.83048306,  0.83042227]), 'std_test_score': array([ 0.00893708,  0.07534904,  0.06837527,  0.06581373,  0.06568985,\n",
      "        0.06532913,  0.06563107,  0.06595403]), 'rank_test_score': array([8, 7, 6, 4, 5, 1, 2, 3]), 'split0_train_score': array([ 0.71885211,  0.83804868,  0.85218206,  0.85324587,  0.85324587,\n",
      "        0.85342317,  0.85271396,  0.85334718]), 'split1_train_score': array([ 0.71788962,  0.83204579,  0.84678706,  0.84843342,  0.84790152,\n",
      "        0.84823079,  0.84838277,  0.8485854 ]), 'split2_train_score': array([ 0.70864466,  0.81890023,  0.83260302,  0.83460399,  0.83432537,\n",
      "        0.83483194,  0.83462932,  0.83447734]), 'split3_train_score': array([ 0.70785948,  0.80438692,  0.82353537,  0.82662547,  0.82591626,\n",
      "        0.82682809,  0.82581495,  0.82672678]), 'split4_train_score': array([ 0.70137271,  0.78558403,  0.80723838,  0.81075879,  0.81040421,\n",
      "        0.81040421,  0.81030291,  0.81042954]), 'mean_train_score': array([ 0.71092371,  0.81579313,  0.83246918,  0.83473351,  0.83435865,\n",
      "        0.83474364,  0.83436878,  0.83471325]), 'std_train_score': array([ 0.00659073,  0.01903578,  0.01618656,  0.0153032 ,  0.01539627,\n",
      "        0.01540064,  0.01539701,  0.01544796])}\n",
      "time.struct_time(tm_year=2018, tm_mon=6, tm_mday=9, tm_hour=7, tm_min=22, tm_sec=40, tm_wday=5, tm_yday=160, tm_isdst=0)\n"
     ]
    }
   ],
   "source": [
    "print(time.localtime(time.time()))\n",
    "grid_search_lg.fit(train_data,train_label)\n",
    "\n",
    "print(grid_search_lg.best_score_)\n",
    "print(grid_search_lg.best_params_)\n",
    "print(grid_search_lg.cv_results_)\n",
    "print(time.localtime(time.time()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  计算log loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.370617241236\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "x_train,x_test,y_train,y_test = train_test_split(train_data,train_label,test_size=0.2,random_state=1)\n",
    "#logis = LogisticRegression(C=100,multi_class='multinomial',penalty='l2',solver='lbfgs',random_state=1)\n",
    "y_pred = grid_search_lg.predict_proba(x_test)\n",
    "print(log_loss(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 SVM测试及调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "svc = SVC(kernel='rbf',decision_function_shape='ovo',random_state=1,cache_size = 1000)\n",
    "#svc = SVC(kernel='rbf',decision_function_shape='ovo',random_state=1,cache_size = 1000)\n",
    "c = np.logspace(-3, -1, 6)\n",
    "gamma_s = [  1.00000000e-05,   1.00000000e-04,   1.00000000e-03,   1.00000000e-02] #np.logspace(-5, -2, 4)\n",
    "#gamma_s = [0.0001]\n",
    "tuned_parameters = dict(gamma = gamma_s, C = c)\n",
    "grid_search_svm = GridSearchCV(svc,tuned_parameters,cv=3,return_train_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 模型训练和得分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time.struct_time(tm_year=2018, tm_mon=6, tm_mday=9, tm_hour=7, tm_min=23, tm_sec=45, tm_wday=5, tm_yday=160, tm_isdst=0)\n",
      "0.734093856379\n",
      "{'C': 0.10000000000000001, 'gamma': 0.01}\n",
      "{'mean_fit_time': array([  62.41028515,   72.90772112,  100.76921662,  116.1691618 ,\n",
      "         65.11057035,  107.28577709,  103.46414868,   94.27857097,\n",
      "         60.63228893,   99.06055172,  114.17954477,   99.99592153,\n",
      "         83.03020557,   95.43453709,   96.2928346 ,   82.69765385,\n",
      "         81.77423684,   85.32569194,   84.65925622,   82.84739161,\n",
      "         93.17376542,  109.04475665,  101.4351639 ,   92.51710947]), 'std_fit_time': array([  2.03068433,   2.29629716,   7.99045509,   6.68443152,\n",
      "         5.70878165,   3.27829546,  11.47138581,   3.41892429,\n",
      "         1.0870628 ,  11.90691989,   2.58289517,   2.49399855,\n",
      "        10.87959334,   3.65629226,   1.93122579,   0.60970279,\n",
      "         2.66618174,   1.6232057 ,   0.9162923 ,   1.9038457 ,\n",
      "         9.75654981,   3.24803354,   2.60267705,   3.64255396]), 'mean_score_time': array([ 17.76818768,  18.75895651,  17.98116676,  21.0415012 ,\n",
      "        17.26315022,  20.43212159,  17.05707685,  16.35389694,\n",
      "        16.36453287,  16.96046654,  16.99367197,  16.2094535 ,\n",
      "        16.41302395,  15.67838717,  15.90599664,  14.22686974,\n",
      "        14.06575052,  14.10586786,  14.26628455,  14.25057594,\n",
      "        15.42697159,  16.44240808,  17.01860698,  15.7385335 ]), 'std_score_time': array([  9.09810709e-01,   2.12149840e-01,   1.51228780e+00,\n",
      "         1.22469963e+00,   2.08935205e+00,   1.17353071e+00,\n",
      "         1.62856574e+00,   8.66575596e-01,   5.64767554e-02,\n",
      "         2.05002040e+00,   8.65056445e-01,   7.47301999e-02,\n",
      "         1.23788235e-01,   2.34499509e-01,   5.17956099e-01,\n",
      "         3.10837646e-02,   3.10914472e-02,   4.25692816e-02,\n",
      "         1.89065159e-03,   3.31565531e-02,   9.98292614e-01,\n",
      "         1.31457877e-01,   6.29613501e-01,   1.06048646e+00]), 'param_C': masked_array(data = [0.001 0.001 0.001 0.001 0.0025118864315095794 0.0025118864315095794\n",
      " 0.0025118864315095794 0.0025118864315095794 0.0063095734448019303\n",
      " 0.0063095734448019303 0.0063095734448019303 0.0063095734448019303\n",
      " 0.015848931924611141 0.015848931924611141 0.015848931924611141\n",
      " 0.015848931924611141 0.039810717055349734 0.039810717055349734\n",
      " 0.039810717055349734 0.039810717055349734 0.10000000000000001\n",
      " 0.10000000000000001 0.10000000000000001 0.10000000000000001],\n",
      "             mask = [False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False],\n",
      "       fill_value = ?)\n",
      ", 'param_gamma': masked_array(data = [1e-05 0.0001 0.001 0.01 1e-05 0.0001 0.001 0.01 1e-05 0.0001 0.001 0.01\n",
      " 1e-05 0.0001 0.001 0.01 1e-05 0.0001 0.001 0.01 1e-05 0.0001 0.001 0.01],\n",
      "             mask = [False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False],\n",
      "       fill_value = ?)\n",
      ", 'params': [{'C': 0.001, 'gamma': 1e-05}, {'C': 0.001, 'gamma': 0.0001}, {'C': 0.001, 'gamma': 0.001}, {'C': 0.001, 'gamma': 0.01}, {'C': 0.0025118864315095794, 'gamma': 1e-05}, {'C': 0.0025118864315095794, 'gamma': 0.0001}, {'C': 0.0025118864315095794, 'gamma': 0.001}, {'C': 0.0025118864315095794, 'gamma': 0.01}, {'C': 0.0063095734448019303, 'gamma': 1e-05}, {'C': 0.0063095734448019303, 'gamma': 0.0001}, {'C': 0.0063095734448019303, 'gamma': 0.001}, {'C': 0.0063095734448019303, 'gamma': 0.01}, {'C': 0.015848931924611141, 'gamma': 1e-05}, {'C': 0.015848931924611141, 'gamma': 0.0001}, {'C': 0.015848931924611141, 'gamma': 0.001}, {'C': 0.015848931924611141, 'gamma': 0.01}, {'C': 0.039810717055349734, 'gamma': 1e-05}, {'C': 0.039810717055349734, 'gamma': 0.0001}, {'C': 0.039810717055349734, 'gamma': 0.001}, {'C': 0.039810717055349734, 'gamma': 0.01}, {'C': 0.10000000000000001, 'gamma': 1e-05}, {'C': 0.10000000000000001, 'gamma': 0.0001}, {'C': 0.10000000000000001, 'gamma': 0.001}, {'C': 0.10000000000000001, 'gamma': 0.01}], 'split0_test_score': array([ 0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69691812,\n",
      "        0.69466902,  0.69466902,  0.69485138,  0.72032095]), 'split1_test_score': array([ 0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.6956416 ,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.74341985]), 'split2_test_score': array([ 0.69471125,  0.69471125,  0.69471125,  0.69471125,  0.69471125,\n",
      "        0.69471125,  0.69471125,  0.69471125,  0.69471125,  0.69471125,\n",
      "        0.69471125,  0.69471125,  0.69471125,  0.69471125,  0.69471125,\n",
      "        0.69471125,  0.69471125,  0.69471125,  0.69471125,  0.69471125,\n",
      "        0.69471125,  0.69471125,  0.69471125,  0.73854103]), 'mean_test_score': array([ 0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.69468309,\n",
      "        0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.69468309,\n",
      "        0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.69468309,\n",
      "        0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.69575701,\n",
      "        0.69468309,  0.69468309,  0.69474388,  0.73409386]), 'std_test_score': array([  1.99067962e-05,   1.99067962e-05,   1.99067962e-05,\n",
      "         1.99067962e-05,   1.99067962e-05,   1.99067962e-05,\n",
      "         1.99067962e-05,   1.99067962e-05,   1.99067962e-05,\n",
      "         1.99067962e-05,   1.99067962e-05,   1.99067962e-05,\n",
      "         1.99067962e-05,   1.99067962e-05,   1.99067962e-05,\n",
      "         1.99067962e-05,   1.99067962e-05,   1.99067962e-05,\n",
      "         1.99067962e-05,   9.04636514e-04,   1.99067962e-05,\n",
      "         1.99067962e-05,   7.79429097e-05,   9.94064490e-03]), 'rank_test_score': array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 3,\n",
      "       1]), 'split0_train_score': array([ 0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.69469013,\n",
      "        0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.69469013,\n",
      "        0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.69469013,\n",
      "        0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.71991733,\n",
      "        0.69469013,  0.69469013,  0.70791161,  0.80845567]), 'split1_train_score': array([ 0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.69469013,\n",
      "        0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.69469013,\n",
      "        0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.69469013,\n",
      "        0.69469013,  0.69469013,  0.69469013,  0.69469013,  0.69909729,\n",
      "        0.69469013,  0.69469013,  0.69520683,  0.77307681]), 'split2_train_score': array([ 0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.69466902,  0.69466902,\n",
      "        0.69466902,  0.69466902,  0.69466902,  0.70740381]), 'mean_train_score': array([ 0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.69468309,\n",
      "        0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.69468309,\n",
      "        0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.69468309,\n",
      "        0.69468309,  0.69468309,  0.69468309,  0.69468309,  0.70456121,\n",
      "        0.69468309,  0.69468309,  0.69926249,  0.76297876]), 'std_train_score': array([  9.95319641e-06,   9.95319641e-06,   9.95319641e-06,\n",
      "         9.95319641e-06,   9.95319641e-06,   9.95319641e-06,\n",
      "         9.95319641e-06,   9.95319641e-06,   9.95319641e-06,\n",
      "         9.95319641e-06,   9.95319641e-06,   9.95319641e-06,\n",
      "         9.95319641e-06,   9.95319641e-06,   9.95319641e-06,\n",
      "         9.95319641e-06,   9.95319641e-06,   9.95319641e-06,\n",
      "         9.95319641e-06,   1.10078795e-02,   9.95319641e-06,\n",
      "         9.95319641e-06,   6.11979547e-03,   4.18676306e-02])}\n",
      "time.struct_time(tm_year=2018, tm_mon=6, tm_mday=9, tm_hour=10, tm_min=15, tm_sec=47, tm_wday=5, tm_yday=160, tm_isdst=0)\n"
     ]
    }
   ],
   "source": [
    "print(time.localtime(time.time()))\n",
    "grid_search_svm.fit(train_data,train_label)\n",
    "\n",
    "print(grid_search_svm.best_score_)\n",
    "print(grid_search_svm.best_params_)\n",
    "print(grid_search_svm.cv_results_)\n",
    "print(time.localtime(time.time()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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